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Conversational AI with Rasa

You're reading from   Conversational AI with Rasa Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801077057
Length 264 pages
Edition 1st Edition
Tools
Concepts
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Authors (2):
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Xiaoquan Kong Xiaoquan Kong
Author Profile Icon Xiaoquan Kong
Xiaoquan Kong
Guan Wang Guan Wang
Author Profile Icon Guan Wang
Guan Wang
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: The Rasa Framework
2. Chapter 1: Introduction to Chatbots and the Rasa Framework FREE CHAPTER 3. Chapter 2: Natural Language Understanding in Rasa 4. Chapter 3: Rasa Core 5. Section 2: Rasa in Action
6. Chapter 4: Handling Business Logic 7. Chapter 5: Working with Response Selector to Handle Chitchat and FAQs 8. Chapter 6: Knowledge Base Actions to Handle Question Answering 9. Chapter 7: Entity Roles and Groups for Complex Named Entity Recognition 10. Chapter 8: Working Principles and Customization of Rasa 11. Section 3: Best Practices
12. Chapter 9: Testing and Production Deployment 13. Chapter 10: Conversation-Driven Development and Interactive Learning 14. Chapter 11: Debugging, Optimization, and Community Ecosystem 15. Other Books You May Enjoy

What this book covers

Chapter 1, Introduction to Chatbots and the Rasa Framework, introduces all the fundamental knowledge pertaining to chatbots and the Rasa framework, including machine learning, NLP, chatbots, and Rasa Basic.

Chapter 2, Natural Language Understanding in Rasa, covers Rasa NLU’s architecture, configuration methods, and how to train and infer.

Chapter 3, Rasa Core, introduces how to implement dialogue management in Rasa.

Chapter 4, Handling Business Logic, explains how Rasa gives developers great flexibility in handling different business logic. This chapter introduces how we can use these features to handle complex business logic more elegantly and efficiently.

Chapter 5, Working with Response Selector to Handle Chitchat and FAQs, explains how to define questions and their corresponding answers and how to configure Rasa to automatically identify the query and give the corresponding answer.

Chapter 6, Knowledge Base Actions to Handle Question Answering, describes how to create a knowledge base that will be used to answer questions. You will also learn to customize knowledge base actions, learn how referential resolution (mapping mention to object) works, and how to create your own knowledge base.

Chapter 7, Entity Roles and Groups for Complex Named Entity Recognition, explains how entity roles and entity groups solve the complex NER problem, and how to define training data, configure pipelines, and write stories for entity roles and entity groups.

Chapter 8, Working Principles and Customization of Rasa, introduces the working principles behind Rasa and how we can extend and customize Rasa.

Chapter 9, Testing and Production Deployment, explains how to test Rasa applications and how to deploy Rasa applications in production environments.

Chapter 10, Conversation-Driven Development and Interactive Learning, introduces conversation-driven development and Rasa X to develop chatbots more effectively. We will also introduce how to use interactive learning to quickly find and fix problems.

Chapter 11, Debugging, Optimization, and Community Ecosystem, explains how to debug and optimize Rasa applications. We will also introduce some tools to help developers build chatbots effectively.

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